Replication Code

The Effects of Shifting Priorities and Capacity on Elected Officials’ Policy Work and Constituency Service: Evidence from a Census of Legislator Requests to U.S. Federal Agencies

Replication data

From code/replication.R, which starts with the main data file (dcounts_min.Rdata) and makes transformations for three sets of models:

1. counts per member-year (dcounts_tenure.Rdata/AgencyComm.dta)
2. ratio per member-year (dcounts_ratio.Rdata/ProportionContact.dta)
3. counts per district-year (dcounts_per_district.Rdata, DistrictLevel.dta)

The same three datasets are used in Replication.do

load(here::here("data", "dcounts_tenure.Rdata"))
load(here::here("data", "dcounts_ratio.Rdata"))
load(here::here("data", "dcounts_per_district.Rdata"))
load(here::here("data" , "members.Rdata"))


# var names for tables
dcounts_tenure %<>% 
  mutate(Legislator = icpsr,
         Legislator_x_Agency = icpsr_agency,
         Year_x_Agency = agency_year)

# total per agency
dcounts_tenure %>% group_by(agency) %>% summarise(n = perYear %>% sum()) %>% arrange(-n) %>% kablebox()

agency	n
VA	76868
DHHS_CMS	67613
HUD_HQ	33838
DOL_SOL	27065
DHS_HQ	26294
USPS	17084
FTC	13417
DOT_FAA	12235
Treasury_IRS	11591
Treasury_Fiscal	11233
EPA	10956
DHHS_HRSA	10810
DOL_OWCP	10710
DOL_EBSA	10018
DHHS_FDA	9719
NARA	9690
DOT_FHWA	9224
DHHS_NIH	7226
EOP_USTR	7188
DHHS_CDC	6390
FCC	5838
USDA	5058
DOC_SBA	4679
DOD_Navy	4606
ED	4573
SSA	4439
DOI_NPS	4248
NASA	2925
FHFA	2879
DOE_FERC	2572
DOT_FRA	2545
USDA_RD	2486
RRB	2377
DOT_FTA	2186
DOD_OSDJS	1905
DOL_OSHA	1893
DOD_OIG	1575
DOC_OS	1536
Amtrak	1500
DOI_BOEM	1398
DOJ_CIV	1288
Treasury_OCC	1285
DHHS_IHS	1223
DOL_VETS	992
DOL_MSHA	959
DOC_EDA	956
CNCS	854
USDA_FS	718
DOD_DFAS	705
DOJ_EOIR	693
DHHS_ACF	691
USDA_RMA	668
DHS_ICE	656
NLRB	634
DOJ_ENRD	613
USDA_NRCS	569
EEOC	566
DOD_DeCA	552
DOT_PHMSA	478
DOL_OFCCP	462
DOL_OASAM	397
PRC	390
NCUA	356
DOC_IOS	347
DOD_USACE	346
DOC_NIST	274
EOP_CEQ	240
DOD_DLA_Aviation	226
DOI_USGS	200
USDA_NIFA	127
DOC_NTIA	119
DHHS_ACL	114
DOI_BSEE	89
TVA	83
USDA_NASS	81
ABMC	78
DOI_SOL	77
FCA	69
DOC_NOAA	56
DOC_OCPA	53
DOI_OSMRE	48
USDA_ERS	44
CSOSA	32
VA_CEM	20
DOT_SLSDC	17
DOL_OCFO	16
NCPC	12
NWTRB	10
DOC_MBDA	9
DOI_NIGC	6

# total per legislator-agency pair 
dcounts_tenure %>% group_by(Legislator_x_Agency) %>% summarise(n = perYear %>% sum()) %>% arrange(-n) %>% kablebox()

Legislator_x_Agency	n
VA_15039	2159
VA_94659	1662
DHHS_CMS_40909	1572
VA_14858	1407
VA_29718	1233
VA_20530	1128
VA_29935	1087
VA_14440	1070
DHS_HQ_14858	1058
VA_40915	1054
VA_29512	1029
VA_14869	949
VA_15021	934
VA_29566	855
DHHS_CMS_20336	853
DHHS_CMS_14226	769
DHHS_CMS_14921	717
VA_15408	698
HUD_HQ_14651	693
VA_40305	687
DHHS_CMS_14506	684
NARA_41308	649
VA_20330	646
DHHS_CMS_14503	632
DHHS_CMS_14440	618
VA_14226	597
VA_1366	575
VA_40706	556
DHHS_CMS_29548	552
HUD_HQ_49300	542
VA_21328	541
HUD_HQ_29909	539
DHHS_CMS_41102	536
DHS_HQ_15000	532
DHHS_CMS_94659	529
DHS_HQ_40706	526
HUD_HQ_20735	525
VA_39305	522
NARA_21111	515
VA_29771	512
DHHS_CMS_39308	480
DHHS_CMS_20143	471
DHHS_CMS_21103	470
DHS_HQ_14824	469
DHHS_CMS_49703	468
DHS_HQ_14651	468
DHHS_CMS_29389	465
VA_20735	463
DHHS_CMS_14871	462
DHHS_CMS_20530	457
DHHS_CMS_49700	456
DHHS_CMS_40304	454
VA_21555	454
DHHS_CMS_15124	451
DOL_SOL_40909	451
VA_39308	447
VA_41502	447
DHHS_CMS_14651	438
DHHS_CMS_14869	438
DHHS_CMS_20115	427
VA_29754	421
DHHS_CMS_40706	420
HUD_HQ_14824	419
DHHS_CMS_14858	417
VA_20115	416
DHHS_CMS_15446	415
DHHS_CMS_21358	415
VA_29909	411
DHHS_CMS_40703	407
DHHS_CMS_20144	404
DHHS_CMS_21191	401
VA_40700	397
DHS_HQ_29368	385
DHHS_CMS_21123	381
USPS_14226	376
VA_21513	376
VA_40906	375
DHHS_CMS_29142	373
VA_14709	373
HUD_HQ_15011	371
DHHS_CMS_40915	370
DHS_HQ_20530	370
DHHS_CMS_15054	367
DHS_HQ_14226	367
HUD_HQ_14858	359
DOL_EBSA_14651	358
DHHS_CMS_20346	352
VA_29368	351
DOL_SOL_14651	350
FTC_14651	349
VA_20343	348
DHHS_CMS_15039	346
VA_14873	346
VA_20713	345
DHS_HQ_40305	338
DHHS_CMS_90327	334
DHHS_CMS_15703	331
VA_15424	326
DOL_SOL_40706	325
DHHS_CMS_20947	323

Values to predict at

The most frequent legislator agency pair: McCain x VA with 2159 letters!

The average is 73 letters per legislator per year, a little over 1.29 letters per agency per year.

For predictions, we need an average member-year-agency triad. The first table below shows the members who averaged 73, entered Congress between 2007 and 2011, were observed in our data for at least seven years, and most often submitted exactly one letter to an agency (the closest integer 1.29). For realism, this person would ideally, at some point in our data, become a chair. Of the modal legislators, Phil Roe meets this last criterion. The IRS is a fairly modal agency and one of the agencies to which Phil Roe submitted one letter in 2015, so predictions are at Phil Roe X IRS X 2015. Other legislators, years, and agencies are simply intercept shifts.

modal <- dcounts_tenure %>% 
  group_by(icpsr) %>% 
  filter(first_year > 2006, first_year < 2013, max_year > 6) %>% 
  mutate(Chair = sum(chair, na.rm = T) >1) %>% 
  group_by(icpsr, year) %>%
  mutate(perLeg = sum(perYear)) %>% 
  filter(perYear == 1, perLeg == 73 ) %>% 
  select(perAgency = perYear, perLeg, icpsr, icpsr_agency, agency_year, first_year, max_year, Chair) %>% 
  distinct() %>% 
  ungroup() %>% left_join(members %>% select(bioname, icpsr))

modal %>%
  mutate(average = perLeg) %>% 
  count(average, bioname, first_year, max_year, Chair, sort = T, name = "agency_year_count == 1") %>% 
  kablebox()

average	bioname	first_year	max_year	Chair	agency_year_count == 1
73	COONS, Christopher A.	2009	9	FALSE	78
73	MERKLEY, Jeff	2009	9	FALSE	66
73	ROE, David P. (Phil)	2009	9	TRUE	66
73	HELLER, Dean	2007	11	FALSE	63
73	TITUS, Alice (Dina)	2009	9	FALSE	60
73	BRALEY, Bruce	2007	7	FALSE	52
73	PETERS, Gary C.	2009	9	FALSE	42
73	FARENTHOLD, Blake	2011	7	FALSE	36
73	SHEA-PORTER, Carol	2007	11	FALSE	16

modal %<>% filter(Chair)

dcounts_tenure %>% 
  group_by(icpsr, year) %>% 
  mutate (perLegislator = sum(perYear)) %>% 
  ungroup() %>% 
  mutate(meanLeg = mean(perLegislator),
         meanYear = mean(perYear)) %>%
  filter( abs(perLegislator - meanLeg) < 10,   
          abs(perYear - meanYear) < 10 )  %>%
  ggplot() +
  aes(x = perYear, y = perLegislator) + 
    geom_jitter() + 
  geom_vline(aes(xintercept = meanYear), color = "blue") + 
  geom_hline(aes(yintercept = meanLeg), color = "blue") + 
  geom_text_repel(aes(label = ifelse(icpsr_agency %in% modal$icpsr_agency & agency_year %in% modal$agency_year & perLegislator == 73, icpsr_agency, NA) ),
                  color = "red", box.padding = 0.5, max.overlaps = Inf) + 
  labs(x = "Contacts per Legislator per Agency per Year",
       y = "Contacts per Legislator per Year")

modal %>%
  distinct(perAgency, bioname, icpsr_agency, agency_year) %>% 
  kablebox()

perAgency	icpsr_agency	agency_year	bioname
1	DHHS_CDC_20947	DHHS_CDC_2015	ROE, David P. (Phil)
1	DHHS_HRSA_20947	DHHS_HRSA_2015	ROE, David P. (Phil)
1	DHHS_NIH_20947	DHHS_NIH_2015	ROE, David P. (Phil)
1	DOD_USACE_20947	DOD_USACE_2015	ROE, David P. (Phil)
1	DOI_NPS_20947	DOI_NPS_2015	ROE, David P. (Phil)
1	DOL_OSHA_20947	DOL_OSHA_2015	ROE, David P. (Phil)
1	DOT_FRA_20947	DOT_FRA_2015	ROE, David P. (Phil)
1	NARA_20947	NARA_2015	ROE, David P. (Phil)
1	Treasury_Fiscal_20947	Treasury_Fiscal_2015	ROE, David P. (Phil)
1	Treasury_IRS_20947	Treasury_IRS_2015	ROE, David P. (Phil)
1	USDA_RMA_20947	USDA_RMA_2015	ROE, David P. (Phil)

## other variable means
# dcounts_tenure %>% summarise(across(everything(), mean_or_mode)) %>% kablebox()

# by chair and presidents' party
values <- tidyr::expand(dcounts_tenure,
                        agency = "Treasury_IRS",
                        chair = chair, 
                        ranking_minority    = FALSE, 
                        prestige = TRUE, 
                        first = FALSE,
                        second = FALSE,
                        third = FALSE,
                        fourth = FALSE,
                        fifth = FALSE, 
                        sixth = FALSE,
                        Legislator_x_Agency = "Treasury_IRS_20947",
                        Year_x_Agency = "Treasury_IRS_2015",
                        Legislator = "20947",
                        Year = "2015",
                        majority = TRUE,
                        presidents_party = presidents_party
                   ) %>% 
  drop_na(majority, chair)

# by year 
values_tenure <- tidyr::expand(dcounts_tenure,
                        agency = "Treasury_IRS",
                        chair = chair, 
                        ranking_minority    = FALSE, 
                        prestige = TRUE, 
                        first = first,
                        second = second,
                        third = third,
                        fourth = fourth,
                        fifth = fifth, 
                        sixth = sixth,
                        Legislator_x_Agency = "Treasury_IRS_20947",
                        Year_x_Agency = "Treasury_IRS_2015",
                        Legislator = "20947",
                        Year = "2015",
                        majority = TRUE,
                        presidents_party = TRUE
                   ) %>% 
  drop_na(chair)

# helper functions to plot predicted values
ggchair <- function(predicted = predicted) {
  
  predicted$chair %<>% str_replace("0", "Not Chair") %>% str_replace("1", "Chair")
  
  predicted$presidents_party %<>% str_replace("0", "Not President's Party") %>% str_replace("1", "President's Party")

predicted %>%  
  ggplot() + 
  aes(x = "", 
      y = predicted, 
      fill = chair,
      shape = chair,
      color = chair,
      ymin = predicted - 1.96*std.error,
      ymax = predicted + 1.96*std.error) + 
  geom_pointrange(position =  position_dodge(width = -.5)  )  + 
  scale_fill_viridis_d(begin = 0, end = .6, option = "cividis") +
  scale_color_viridis_d(begin = 0, end = .6, option = "cividis") +
      theme_minimal() + 
  theme(panel.border  = element_blank(),
        panel.grid.major.x = element_blank()) + facet_grid(. ~ presidents_party) 
}


ggtenure <- function(predicted = predicted) {
  
predicted %<>%
  # drop estimates from impossible values
      group_by(rowid, predicted, std.error, chair) %>% 
  mutate(sum = sum(first, second, third, fourth, fifth, sixth),
         more = ifelse(sum == 0, 1,0)) %>% 
    filter(sum < 2) %>% 
    pivot_longer(cols = c("first", "second", "third", "fourth", "fifth", "sixth", "more")) %>% 
    select(name, value) %>% 
    filter(value == 1) %>% 
    # clean up for presentation
    mutate(year_in_congress = name %>% 
             str_replace("more", "7\nor more") %>% 
             str_replace("sixth", "6") %>% 
             str_replace("fifth", "5") %>% 
             str_replace("fourth", "4") %>% 
             str_replace("third", "3") %>% 
             str_replace("second", "2") %>% 
             str_replace("first", "1") 
             ) %>% 
    ungroup()
  
# Ideally, we could plot against data, but there is so much variation that you can no longer distinguish differences in predicted values 
# predicted %<>% full_join(dcounts_tenure2 %>% mutate(predicted = NA))
  
predicted %<>% filter(chair == 0 | name %in% c("sixth", "more"))

  predicted$chair %<>% str_replace("0", "Not Chair") %>% str_replace("1", "Chair")
  

predicted %>%  
  ungroup() %>% 
  ggplot() + 
  aes(x = year_in_congress, 
      y = predicted, 
      shape = chair,
      color = chair,
      ymin = predicted - 1.96*std.error,
      ymax = predicted + 1.96*std.error) + 
  geom_pointrange(position =  position_dodge(width = -.3)  )  + 
  scale_fill_viridis_d(begin = 0, end = .6, option = "cividis") +
  scale_color_viridis_d(begin = 0, end = .6, option = "cividis") +
      theme_minimal() + 
  theme(panel.border  = element_blank(),
        panel.grid.major.x = element_blank()) 
  
}

---

Counts per member per year

Clustering standard errors at the legislator level:

“Clustering on the panel variable produces an estimator of the VCE that is robust to cross-sectional heteroskedasticity and within-panel (serial) correlation that is asymptotically equivalent to that proposed by Arellano (1987)”

Replicated using fixest::feglm here and xtreg in Replication.do. See more about robust (clustered) standard errors with fixed effects are calculated by fixest here and by xtreg here.

# Coef Map
cm = c("chair" = "Committee Chair",
       "ranking_minority" = "Ranking Member",
       "prestige" = "Prestige Committee",
       "first" = "First Year",
       "second" = "Second Year",
       "third" = "Third Year",
       "fourth" = "Fourth Year",
       "fifth" = "Fifth Year",
       "sixth" = "Sixth Year",
       "majority" = "Majority",
       "presidents_party" = "President's Party",
       "Legislator" = "Legislator", 
       "Agency" = "Agency",
       "FE: icpsr_agency" = "Legislator × Agency Fixed Effects",
       "icpsr_agency" = "Legislator × Agency Fixed Effects")

coef_omit = "(Intercept)|majority|presidents_party"


setFixest_dict(cm)

gof_omit = "R.*|AIC|BIC|Log.*|Std.*"

# table formatting to match stata (sort of)
format_table <- . %>% row_spec(row = 1, bold = T, hline_after = TRUE) %>% 
      kable_styling(font_size = 11) %>% 
                    #full_width = TRUE, 
                    #latex_options = c("repeat_header")) %>%
  str_replace("Num.Obs.", "Observations") %>% 
  str_replace("Std.Errors", "Robust/Clustered Std. Errors") %>% 
  str_replace("FE: Legislator_x_Agency", "Legislator x Agency FE") %>%
  str_replace("FE: Year_x_Agency", "Year x Agency FE") %>% 
  str_replace("FE: Year", "Year Fixed Effects") %>% 
  str_replace("FE: Legislator", "Legislator Fixed Effects") %>%
    str_replace_all("X|✓", "\\\\\\checkmark") %>% 
  #  extract just the table, no caption etc
  str_extract("\\\\begin\\{tabular\\}(\n|.)*tabular\\}") 

---

Total Letters

• Figures: figs/m-total-[1:4].png

testing = F

# paper table 
# 1 
# cross-sectional 
m_total_cross <-feglm (perYear ~ 
                         chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency,
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_total_cross, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_total_cross, horiz = T, drop = "(Intercept)") 



# 2
m_total_dnd <-feglm (perYear ~ 
                       chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator",
                    # ALT vcov = hetero ~ ssc(cluster.adj = TRUE),
           data = dcounts_tenure)

if(testing){modelsummary(m_total_dnd, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_total_dnd, horiz = T) 


# 3
m_total_2nd <-feglm (perYear ~ 
                       chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure %>% filter(survive == 1))

if(testing){modelsummary(m_total_2nd, gof_omit = "R.*", coef_omit = coef_omit)}
coefplot(m_total_2nd, horiz = T) 


# 4
m_logtotal_dnd <-feglm (log(perYear + 1) ~ 
                          chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_logtotal_dnd, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_logtotal_dnd, horiz = T) 

Total Letters Per Year

Total Letters Table

• Table: tables/models_total.tex (sans stars)

models_total <- list(
  "(1)" = m_total_cross,
  "(2)" = m_total_dnd,
  "(3)" =  m_total_2nd,
  "(4)" = m_logtotal_dnd
)

rows <- tibble(
  term = c("Dependent Variable", 
           "Majority",
           "Presidents` Party",
           "All Legislators", 
           "Served At Least 2nd Term"),
  `(1)` = c("Count", "✓", "✓", "✓", ""),
  `(2)` =c("Count", "✓", "✓", "✓", ""),
  `(3)` = c("Count", "✓","✓","", "✓"),
  `(4)` = c("Log(Count+1)","✓","✓", "✓", "") 
)

attr(rows, 'position') <- c(0, 20,21,22,23)


# HTML
modelsummary(models_total, stars = T, 
             add_rows = rows, 
             coef_map = cm,
             gof_omit = gof_omit,
             coef_omit = coef_omit,
             title = "The Effect of Expierence and Institutional Power on Legislator Contacts", 
             notes = list("Robust standard errors in parentheses, clustered by legislator."))

The Effect of Expierence and Institutional Power on Legislator Contacts

Dependent Variable	Count	Count	Count	Log(Count+1)
Committee Chair	0.715***	0.271**	0.275**	0.049***
	(0.151)	(0.090)	(0.090)	(0.012)
Ranking Member	0.842***	0.153	0.170+	0.031**
	(0.154)	(0.094)	(0.094)	(0.010)
Prestige Committee	0.469***	0.100+	0.093+	0.026**
	(0.067)	(0.051)	(0.052)	(0.010)
First Year	−0.301***	−0.512***	−0.494***	−0.103***
	(0.053)	(0.075)	(0.073)	(0.012)
Second Year	−0.067	−0.275***	−0.291***	−0.042***
	(0.060)	(0.072)	(0.072)	(0.011)
Third Year	−0.046	−0.189**	−0.208***	−0.030**
	(0.063)	(0.061)	(0.060)	(0.009)
Fourth Year	0.026	−0.135*	−0.158**	−0.018*
	(0.067)	(0.060)	(0.057)	(0.009)
Fifth Year	−0.046	−0.135**	−0.139***	−0.024***
	(0.059)	(0.043)	(0.042)	(0.007)
Sixth Year	0.049	−0.029	−0.011	−0.014*
	(0.073)	(0.056)	(0.054)	(0.007)
Majority	✓	✓	✓	✓
Presidents` Party	✓	✓	✓	✓
All Legislators	✓	✓		✓
Served At Least 2nd Term			✓
Num.Obs.	412111	412111	388997	412111
FE: Year_x_Agency	X	X	X	X
FE: Legislator_x_Agency		X	X	X
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Robust standard errors in parentheses, clustered by legislator.

# TeX 
modelsummary(models_total, # stars = T, 
             add_rows = rows,               
             coef_map = cm, 
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             output = "latex",
             title = "\\\\footnotesize The Effect of Expierence and Institutional Power on Legislator Contacts", 
             notes = list("\\\\footnotesize Robust standard errors in parentheses, clustered by legislator.")) %>% 
  format_table() %>%
  write_lines(file = here::here("tables", "models_total.tex") )


beta <- m_total_dnd$coefficients %>%
  round(3) %>% 
  as_tibble(rownames = "beta") %>% 
  pivot_wider(names_from = beta)

se <- m_total_dnd$se %>%
  round(3) %>% as_tibble(rownames = "se") %>%
  pivot_wider(names_from = se)

Total Letters Narrative

To address potential confounding in across-legislator comparisons, the estimates from Model 2 (Column 2 of Table \ref{tab:models_total}) provides the estimated effects from the difference-in-differences specification in Equation \ref{e:diff1}. Across all measures of institutional power, we find that more power increases the number of requests that legislators make. Consider first the effect of being a committee chair. We estimate that becoming a committee chair causes an increase of 0.27 requests \textit{per agency} (95-percent confidence interval [0.09, 0.45]). Across all 90 agencies, this represents an increase of approximately 24 additional requests per year, 24.1\% of the average requests per year in our data. There is a smaller increase for individuals who become ranking members and those who join a Prestige Committee, though the increase is statistically significant for the prestige committee. Becoming a ranking member of a committee causes an increase of 0.15 contacts per agency, while joining a prestige committee causes a 0.27 per agency increase in the number of contacts a member of Congress makes.

We estimate that the experience gained between the first and second year in Congress causes an increase of 0.24 requests \textit{per agency}. The experience gained between the first and seventh year causes an increase of 0.51 per agency. Across all 90 agencies, this represents an increase of approximately 46 additional requests per year, 45.5\% of the average requests per year in our data. There is a smaller increase after the second year. The experience gained between the second and seventh year causes an increase of 0.28 per agency, an increase of approximately 25 additional requests per year, 45.5\% of the average requests per year in our data.

Total Letters Predictions

• Figures: figs/m-total-predicted-[1:4].png

predicted <- predictions(m_total_cross,
                         newdata = values)

# Cross-sectional predictions
predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() + 
  labs(title = "Predicted Total Letters per Year\n(Cross-Sectional)",
       x = "",
       y = "Predicted Total Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

# by tenure
predicted <- predictions(m_total_cross,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>% 
ggtenure() +
  labs(title = "Predicted Total Letters Per Year\n(Cross-Sectional)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 


# diff in diff 
predicted <- predictions(m_total_dnd,
                         newdata = values)


# predictions by chair and presidents' party
predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() + 
  labs(title = "Predicted Total Letters per Year\nDifference in Differences (Within-Legislator)",
       x = "",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

# by tenure
predicted <- predictions(m_total_dnd,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Total Letters Per Year\nDifference in Differences (Within-Legislator)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

Total

Constituency Service

DV: Only letters coded as constituency service letters

• Figures: figs/m-con-[1:4].png

testing = F

m_con_cross <-feglm (perYear_con ~ 
                       chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency,
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_con_cross, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_con_cross, horiz = T, drop = "(Intercept)") 



# 2
m_con_dnd <-feglm (perYear_con ~ 
                     chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator",
           data = dcounts_tenure)

if(testing){modelsummary(m_con_dnd, gof_omit = "R.*", coef_omit = coef_omit)}
coefplot(m_con_dnd, horiz = T) 


# 3
m_con_2nd <-feglm (perYear_con ~ 
                     chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure %>% filter(survive == 1))

if(testing){modelsummary(m_con_2nd, gof_omit = "R.*", coef_omit = coef_omit)}
coefplot(m_con_2nd, horiz = T) 


# 4
m_logcon_dnd <-feglm (log(perYear_con + 1) ~ 
                        chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_logcon_dnd, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_logcon_dnd, horiz = T) 

Constituency Service Letters Per Year

Constituency Service Table

• Table: tables/models_con.tex (sans stars)

models_con <- list(
  "(1)" = m_con_cross,
  "(2)" = m_con_dnd,
  "(3)" =  m_con_2nd,
  "(4)" = m_logcon_dnd
)


modelsummary(models_con, stars = T, 
             add_rows = rows, 
             coef_map = cm,
             gof_omit = gof_omit, 
             coef_omit = coef_omit)

Dependent Variable	Count	Count	Count	Log(Count+1)
Committee Chair	0.302**	0.040	0.044	0.012+
	(0.108)	(0.064)	(0.064)	(0.007)
Ranking Member	0.503***	0.054	0.070	0.012+
	(0.108)	(0.067)	(0.067)	(0.007)
Prestige Committee	0.321***	0.031	0.025	0.013+
	(0.049)	(0.036)	(0.036)	(0.007)
First Year	−0.138***	−0.276***	−0.265***	−0.059***
	(0.040)	(0.055)	(0.054)	(0.008)
Second Year	0.009	−0.128*	−0.142**	−0.019*
	(0.046)	(0.053)	(0.052)	(0.008)
Third Year	0.030	−0.070	−0.088+	−0.011+
	(0.047)	(0.047)	(0.046)	(0.007)
Fourth Year	0.061	−0.055	−0.072	−0.006
	(0.052)	(0.046)	(0.045)	(0.006)
Fifth Year	0.001	−0.069*	−0.064+	−0.011*
	(0.044)	(0.034)	(0.033)	(0.005)
Sixth Year	0.070	0.008	0.018	−0.004
	(0.056)	(0.044)	(0.043)	(0.005)
Majority	✓	✓	✓	✓
Presidents` Party	✓	✓	✓	✓
All Legislators	✓	✓		✓
Served At Least 2nd Term			✓
Num.Obs.	412111	412111	388997	412111
FE: Year_x_Agency	X	X	X	X
FE: Legislator_x_Agency		X	X	X
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

modelsummary(models_con, # stars = T, 
             add_rows = rows,               
             coef_map = cm, 
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             output = "latex",
             notes = list("\\\\footnotesize Robust standard errors in parentheses, clustered by legislator."))%>% 
  format_table() %>%
  write_lines(file = here::here("tables", "models_con.tex") )

beta <- m_con_dnd$coefficients %>%round(3) %>% as_tibble(rownames = "beta") %>% pivot_wider(names_from = beta)

se <- m_con_dnd$se %>%round(3) %>% as_tibble(rownames = "se") %>% pivot_wider(names_from = se)

Constituency Service Narrative

Table is identical to Table except that we subset the data to only legislator requests hand-coded as constituency service. Column 2 of Table \ref{tab:models_con} provides the estimated effects from the difference-in-differences specification in Equation \ref{e:diff1}. More experience increases the level of constituency service that legislators provide. The effect of being a committee chair is positive but not significant at the .05 level. We estimate that the experience gained between the first and second year in Congress causes an increase of 0.15 requests \textit{per agency}. The experience gained between the first and seventh year causes an increase of 0.28 per agency. Across all 90 agencies, this represents an increase of approximately 25 additional requests per year, 38.6\% of the average requests per year in our data. There is a smaller increase after the second year. The experience gained between the second and seventh year causes an increase of 0.13 per agency, an increase of approximately 12 additional requests per year, 38.6\% of the average requests per year in our data.

Constituency Service Predictions

• Figures: figs/m-con-predicted-[1:4].png

# Cross-sectional predictions:
predicted <- predictions(m_con_cross,
                         newdata = values)


# A plot by chair & presidents' party
predicted %>%
  mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() + 
  labs(title = "Predicted Constituency Service Letters per Year\n(Cross-Sectional)",
       x = "",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

# by tenure
predicted <- predictions(m_con_cross,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Constituency Service Letters Per Year\n(Cross Sectional)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 



# Diff in diff predictions 
predicted <- predictions(m_con_dnd,
                         newdata = values)


# diff in diff plot
predicted %>%
  mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() + 
  labs(title = "Predicted Constituency Service Letters per Year\nDifference in Differences (Within-Legislator)",
       x = "",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

# by tenure
predicted <- predictions(m_con_dnd,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Constituency Service Letters Per Year\nDifference in Differences (Within-Legislator)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

Constituency Service

Policy Work

Only using letters coded as constituent letters

• Figures: figs/m-policy-[1:4].png

testing = F

m_policy_cross <-feglm (perYear_policy ~ 
                          chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority +  presidents_party | Year_x_Agency,
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_policy_cross, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_policy_cross, horiz = T, drop = "(Intercept)") 



# 2
m_policy_dnd <-feglm (perYear_policy ~ 
                        chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator",
           data = dcounts_tenure)

if(testing){modelsummary(m_policy_dnd, gof_omit = "R.*", coef_omit = coef_omit)}
coefplot(m_policy_dnd, horiz = T) 


# 3
m_policy_2nd <-feglm (perYear_policy ~ 
                        chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure %>% filter(survive == 1))

if(testing){modelsummary(m_tenure_2nd, gof_omit = "R.*", coef_omit = coef_omit)}
coefplot(m_policy_2nd, horiz = T) 


# 4
m_logcon_dnd <-feglm (log(perYear_policy + 1) ~ 
                        chair + ranking_minority + prestige + 
                    first + second + third + fourth + fifth + sixth + 
                    majority + presidents_party | Year_x_Agency + Legislator_x_Agency, 
                    cluster = "Legislator", 
           data = dcounts_tenure)

if(testing){modelsummary(m_logcon_dnd, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_logcon_dnd, horiz = T) 

Policy Letters Per Year

Policy Work Table

• Table: tables/models_policy.tex (sans stars)

models_policy <- list(
  "(1)" = m_policy_cross,
  "(2)" = m_policy_dnd,
  "(3)" =  m_policy_2nd,
  "(4)" = m_logcon_dnd
)


modelsummary(models_policy, stars = T, 
             add_rows = rows, 
             coef_map = cm,
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             notes = list("Robust standard errors in parentheses, clustered by legislator." ))

Dependent Variable	Count	Count	Count	Log(Count+1)
Committee Chair	0.199***	0.158***	0.159***	0.036***
	(0.027)	(0.034)	(0.034)	(0.007)
Ranking Member	0.145***	0.090***	0.092***	0.025***
	(0.028)	(0.024)	(0.024)	(0.005)
Prestige Committee	0.049***	0.031**	0.031**	0.010**
	(0.009)	(0.010)	(0.010)	(0.003)
First Year	−0.076***	−0.076***	−0.070***	−0.030***
	(0.007)	(0.016)	(0.016)	(0.004)
Second Year	−0.045***	−0.042**	−0.040*	−0.018***
	(0.007)	(0.016)	(0.016)	(0.004)
Third Year	−0.042***	−0.031*	−0.033*	−0.013***
	(0.008)	(0.013)	(0.013)	(0.004)
Fourth Year	−0.021*	−0.011	−0.013	−0.006+
	(0.009)	(0.013)	(0.013)	(0.004)
Fifth Year	−0.022*	−0.009	−0.011	−0.006+
	(0.009)	(0.011)	(0.011)	(0.003)
Sixth Year	−0.011	0.002	0.003	−0.006*
	(0.012)	(0.012)	(0.012)	(0.003)
Majority	✓	✓	✓	✓
Presidents` Party	✓	✓	✓	✓
All Legislators	✓	✓		✓
Served At Least 2nd Term			✓
Num.Obs.	412111	412111	388997	412111
FE: Year_x_Agency	X	X	X	X
FE: Legislator_x_Agency		X	X	X
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Robust standard errors in parentheses, clustered by legislator.

modelsummary(models_policy, # stars = T, 
             add_rows = rows,               
             coef_map = cm, 
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             output = "latex",
             notes = list("\\\\footnotesize Robust standard errors in parentheses, clustered by legislator." )) %>% 
  format_table() %>%
   write_lines(file = here::here("tables", "models_policy.tex") )

Policy Work Predictions

• Figures: figs/m-policy-predicted-[1:4].png

predicted <- predictions(m_policy_cross,
                         newdata = values)

# Cross-sectional predictions
predicted %>%
  mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() +
  labs(title = "Predicted Policy Letters per Year\n(Cross-Sectional)",
       x = "",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

# by tenure
predicted <- predictions(m_policy_cross,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Policy Letters Per Year\n(Cross Sectional)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 


# Predictions for diff in diff plot 
predicted <- predictions(m_policy_dnd,
                         newdata = values)


# diff in diff plot
predicted %>%
  mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
  ggchair() +
  labs(title = "Predicted Policy Letters per Year\nDifference in Differences (Within-Legislator)",
       x = "",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "")  

# by tenure
predicted <- predictions(m_policy_dnd,
                         newdata = values_tenure)

predicted %>%
    mutate(predicted = predicted*90,
         std.error = std.error*90) %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Policy Letters Per Year\nDifference in Differences (Within-Legislator)",
       x = "Years Serving in Congress",
       y = "Predicted Letters Per Year",
       fill = "",
       color = "",
       shape = "") 

beta <- m_policy_dnd$coefficients %>%round(3) %>% as_tibble(rownames = "beta") %>% pivot_wider(names_from = beta)

se <- m_policy_dnd$se %>%round(3) %>% as_tibble(rownames = "se") %>% pivot_wider(names_from = se)

Policy

Policy Work Narrative

Table \ref{tab:models_policy} is identical to Table \ref{tab:models_total} except that we subset the data to only legislator requests hand-coded as policy work. Column 2 of Tableprovides the estimated effects from the difference-in-differences specification in Equation. Across all measures of institutional power, we find that more power increases the level of policy work that legislators provide. Consider first the effect of being a committee chair. We estimate that becoming a committee chair causes an increase of 0.16 policy requests(95-percent confidence interval [0.09, 0.22]). Across all 90 agencies, this represents an increase of approximately 14 additional requests per year, 92.6%` of the average requests per year in our data. There is a smaller increase for individuals who become ranking members and those who join a Prestige Committee, though the increase is statistically significant for the prestige committee. Becoming a ranking member of a committee causes an increase of 0.09 contacts per agency while joining a prestige committee causes a 0.16 per agency increase in the policy requests a member of Congress makes.

Ratio

tibble::tibble(
  priority = seq(.2, .8, by = .2),
  capacity = c(100, 100, 100, 100),
  slope = c(-1)
) %>% 
  ggplot() +
  aes(x = capacity*priority,
      y = capacity*(1-priority) ,
      slope = slope, 
      intercept = capacity,
      label = str_c(capacity*priority, ", ", capacity*(1-priority))) + 
  geom_abline(slope = -1, intercept = c(100, 200), linetype = c(100,200)) + 
    #geom_hline(yintercept = .75, linetype = 2) + 
  #geom_vline(xintercept = .2, linetype = 2) + 
 stat_function(fun =  ~ 4*.x , geom = "line", color = "blue") +
   #stat_function(fun =  ~ 1*.x , geom = "line", linetype = 2, color = "blue") +
  #geom_label(vjust = 0) + 
  geom_point() +
  ylim(c(0,200)) + 
  xlim(c(0, 200)) + 
  labs(x = "Policy Work\n Capacity x Policy Share",
       y = "Constituency Service\n Capacity x (1-Policy Share)")

• Figures: figs/m-ratio-[1:2].png

dcounts_ratio %<>% 
  mutate(Legislator = icpsr,
         Year = year) %>% 
  filter(year >2006 & year < 2019)

m_ratio_cross <- feglm(ratio ~
  chair + ranking_minority + prestige + majority +  presidents_party + 
  first + second + third + fourth + fifth + sixth | Year,
cluster = "Legislator",
data = dcounts_ratio)

if(testing){modelsummary(m_ratio_cross, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_ratio_cross, horiz = T, drop = "(Intercept)") 



# Diff in diff
m_ratio_dnd <- feglm(ratio ~
  chair + ranking_minority + prestige + majority + presidents_party + 
  first + second + third + fourth + fifth + sixth | 
    Legislator + Year,
cluster = "Legislator",
data = dcounts_ratio)

if(testing){modelsummary(m_ratio_dnd, gof_omit = "R.*", coef_omit = coef_omit)}

coefplot(m_ratio_dnd, horiz = T) 

Ratio

Ratio Table

• Table: tables/models_ratio.tex (sans stars)

models_ratio <- list(
  "(1)" = m_ratio_cross,
  "(2)" = m_ratio_dnd
)

rows <- tibble(
  term = c("Dependent Variable", "Majoirty", "President's Party"),
  `(1)` = c("Ratio", "✓", "✓"),
  `(2)` =c("Ratio", "✓", "✓")
)

attr(rows, 'position') <- c(0, 20,21)

modelsummary(models_ratio, stars = T, 
             add_rows = rows, 
             coef_map = cm,
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             notes = list("Robust standard errors in parentheses, clustered by legislator.") )

Dependent Variable	Ratio	Ratio
Committee Chair	−0.070***	−0.071***
	(0.016)	(0.017)
Ranking Member	−0.002	−0.029*
	(0.013)	(0.014)
Prestige Committee	0.022**	−0.004
	(0.008)	(0.009)
First Year	0.057***	0.017
	(0.009)	(0.014)
Second Year	0.064***	0.024+
	(0.008)	(0.013)
Third Year	0.059***	0.021+
	(0.009)	(0.012)
Fourth Year	0.035***	−0.003
	(0.009)	(0.012)
Fifth Year	0.027**	−0.003
	(0.010)	(0.011)
Sixth Year	0.041***	0.012
	(0.009)	(0.010)
Majoirty	✓	✓
President’s Party	✓	✓
Num.Obs.	6442	6442
FE: Year	X	X
FE: Legislator		X
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Robust standard errors in parentheses, clustered by legislator.

modelsummary(models_ratio, # stars = T, 
             add_rows = rows,               
             coef_map = cm, 
             gof_omit = gof_omit, 
             coef_omit = coef_omit,
             output = "latex",
             notes = list("\\\\footnotesize Robust standard errors in parentheses, clustered by legislator.") ) %>% 
  # format to match stata output (sort of)
  format_table() %>%
   write_lines(file = here::here("tables", "models_ratio.tex") )

beta <- m_ratio_dnd$coefficients %>%round(3) %>% as_tibble(rownames = "beta") %>% pivot_wider(names_from = beta)

se <- m_ratio_dnd$se %>%round(3) %>% as_tibble(rownames = "se") %>% pivot_wider(names_from = se)

Ratio Narrative

Column 2 of Table \ref{tab:models_ratio} provides the estimated effects from the difference-in-differences specification. While there is little evidence that time in Congress affects legislator priorities, institutional positions do. We estimate that becoming a committee chair causes the ratio of constituency service to policy work to decrease by 0.07 (95-percent confidence interval [-0.04 , -0.1 ]). Becoming a ranking member of a committee causes a decrease of 0.03 in the ratio.

Predictions

• Figures: figs/m-ratio-predicted-[1:4].png

predicted <- predictions(m_ratio_cross,
                         newdata = values)



# Cross-sectional predictions
predicted %>%
ggchair() +
  labs(title = "Predicted Ratio of Constituent to Policy Work\n(Cross-Sectional)",
       x = "",
       y = "Predicted Ratio of\nConstituent to Policy Work",
       fill = "",
       color = "",
       shape = "")

# by tenure
predicted <- predictions(m_ratio_cross,
                         newdata = values_tenure)

predicted %>%
ggtenure() +
  labs(title = "Predicted Ratio of Constituent to Policy Work\n(Cross-Sectional)",
       x = "Years Serving in Congress",
       y = "Predicted Ratio of\nConstituent to Policy Work",
       fill = "",
       color = "",
       shape = "")


# Predictions for diff in diff plot 
predicted <- predictions(m_ratio_dnd,
                         newdata = values)


# diff in diff plot
predicted %>%
  ggchair() + 
  labs(title = "Predicted Ratio of Constituent to Policy Work\nDifference in Differences (Within-Legislator)",
       x = "",
       y = "Predicted Ratio of\nConstituent to Policy Work",
       fill = "",
       color = "",
       shape = "")

# by tenure
predicted <- predictions(m_ratio_dnd,
                         newdata = values_tenure)

predicted %>%
ggtenure() +
    geom_line(aes(x = as.numeric(year_in_congress %>% str_sub(1,1)))) + 
  labs(title = "Predicted Ratio of Constituent to Policy Work\nDifference in Differences (Within-Legislator)",
       x = "Years Serving in Congress",
       y = "Predicted Ratio of\nConstituent to Policy Work",
       fill = "",
       color = "",
       shape = "")

Ratio

Per District